Delayed Mutual Information

Usage

# S4 method for class 'data.frame'
dmi(
  data,
  target,
  tau = 1:10,
  pred = NULL,
  k = 3,
  base = exp(1),
  normalize = FALSE,
  threads = length(tau),
  ...
)

Arguments

data: Observation data.
target: Integer of column indice for the target variable.
tau: (optional) Step of lag.
pred: (optional) Predictions indices.
k: (optional) Number of nearest neighbors used for evaluation.
base: (optional) Logarithm base of the entropy.
normalize: (optional) Whether to normalize MI values.
threads: (optional) Number of threads used.
...: Additional arguments to absorb unused inputs in method dispatch.

Value

A vector.

References

Fraser, A.M., Swinney, H.L., 1986. Independent coordinates for strange attractors from mutual information. Physical Review A 33, 1134–1140.

Kraskov, A., Stogbauer, H., Grassberger, P., 2004. Estimating mutual information. Physical Review E 69, 066138.

Examples

abun = readr::read_csv(system.file("case/abundance.csv", package = "pc"))
#> Rows: 71 Columns: 3
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ","
#> dbl (3): time, paramecium, didinium
#> 
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
pc::dmi(abun, 2)
#>      tau:1      tau:2      tau:3      tau:4      tau:5      tau:6      tau:7 
#> 0.50563138 0.09094202 0.01664535 0.11659768 0.30769606 0.26047393 0.05159755 
#>      tau:8      tau:9     tau:10 
#> 0.00000000 0.08849267 0.07227334